Collaborative Filtering (CF) is one of the most widely-investigated classes of algorithms used for recommendation. These algorithms generate recommendations based on explicit or implicit interactions between users and items in the system [1]}, [2]} without benefiting from the content information, as opposed to content-based recommendation techniques.
Hence, one limitation of CF algorithms is the problem of popularity bias which causes the popular (i.e., short-head) items to be over-emphasized in the recommendation list. In contrast, the majority of less popular (i.e., long-tail) items do not get enough visibility in the recommendation lists. Tackling popularity bias can make the recommender system more applicable in the real world for various reasons [3]}, [4]}.
A recommender system suffering from popularity bias would result in the market being dominated by a few well-known brands and deprive the discovery of new and unpopular items, which could ignore the interest of users with niche tastes.
